SIMT: A Semantic Interest Modeling Toolkit

ACM Conference on User Modeling, Adaptation and Personalization (UMAP), vol. 39, pp. 75–78

Abstract

In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.

Authors 6

1. Mohamed Amine Chatti

   University of Duisburg-Essen

   Affiliation as printed

   University of Duisburg-Essen

2. Fangzheng Ji

   University of Duisburg-Essen

   Affiliation as printed

   University of Duisburg-Essen

3. Mouadh Guesmi

   University of Duisburg-Essen

   Affiliation as printed

   University of Duisburg-Essen

4. Arham Muslim

   National University of Sciences and Technology

   Affiliation as printed

   National University of Sciences and Technology

5. Ravi Kumar Singh Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

6. Shoeb Joarder

   University of Duisburg-Essen

   Affiliation as printed

   University of Duisburg-Essen